Evolutionary Action-Machine Learning Model Identifies Candidate Genes Associated With Early-Onset Coronary Artery Disease

Authors

Dillon Shapiro
Kwanghyuk Lee
Jennifer Asmussen
Thomas Bourquard
Olivier Lichtarge

Publication Date

9-5-2023

Journal

Journal of the American Heart Association

DOI

10.1161/JAHA.122.029103

PMID

37642027

PMCID

PMC10547338

PubMedCentral® Posted Date

8-29-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Animals, Mice, Coronary Artery Disease, Genome-Wide Association Study, Supervised Machine Learning, Biological Evolution, Machine Learning, Mice, Knockout, coronary artery disease, evolutionary action, gene‐based associations, machine learning, myocardial infarction, Genetic, Association Studies; Coronary Artery Disease; Machine Learning

Abstract

Background Coronary artery disease is a primary cause of death around the world, with both genetic and environmental risk factors. Although genome-wide association studies have linked >100 unique loci to its genetic basis, these only explain a fraction of disease heritability. Methods and Results To find additional gene drivers of coronary artery disease, we applied machine learning to quantitative evolutionary information on the impact of coding variants in whole exomes from the Myocardial Infarction Genetics Consortium. Using ensemble-based supervised learning, the Evolutionary Action-Machine Learning framework ranked each gene's ability to classify case and control samples and identified 79 significant associations. These were connected to known risk loci; enriched in cardiovascular processes like lipid metabolism, blood clotting, and inflammation; and enriched for cardiovascular phenotypes in knockout mouse models. Among them,